The PHREG Procedure
- Overview
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Getting Started
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SyntaxPROC PHREG StatementASSESS StatementBASELINE StatementBAYES StatementBY StatementCLASS StatementCONTRAST StatementEFFECT StatementESTIMATE StatementFREQ StatementHAZARDRATIO StatementID StatementLSMEANS StatementLSMESTIMATE StatementMODEL StatementOUTPUT StatementProgramming StatementsRANDOM StatementSTRATA StatementSLICE StatementSTORE StatementTEST StatementWEIGHT Statement
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DetailsFailure Time DistributionTime and CLASS Variables UsagePartial Likelihood Function for the Cox ModelCounting Process Style of InputLeft-Truncation of Failure TimesThe Multiplicative Hazards ModelProportional Rates/Means Models for Recurrent EventsThe Frailty ModelProportional Subdistribution Hazards Model for Competing-Risks DataHazard RatiosNewton-Raphson MethodFirth’s Modification for Maximum Likelihood EstimationRobust Sandwich Variance EstimateTesting the Global Null HypothesisType 3 Tests and Joint TestsConfidence Limits for a Hazard RatioUsing the TEST Statement to Test Linear HypothesesAnalysis of Multivariate Failure Time DataModel Fit StatisticsSchemper-Henderson Predictive MeasureResidualsDiagnostics Based on Weighted ResidualsInfluence of Observations on Overall Fit of the ModelSurvivor Function EstimatorsCaution about Using Survival Data with Left TruncationEffect Selection MethodsAssessment of the Proportional Hazards ModelThe Penalized Partial Likelihood Approach for Fitting Frailty ModelsSpecifics for Bayesian AnalysisComputational ResourcesInput and Output Data SetsDisplayed OutputODS Table NamesODS Graphics
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ExamplesStepwise RegressionBest Subset SelectionModeling with Categorical PredictorsFirth’s Correction for Monotone LikelihoodConditional Logistic Regression for m:n MatchingModel Using Time-Dependent Explanatory VariablesTime-Dependent Repeated Measurements of a CovariateSurvival CurvesAnalysis of ResidualsAnalysis of Recurrent Events DataAnalysis of Clustered DataModel Assessment Using Cumulative Sums of Martingale ResidualsBayesian Analysis of the Cox ModelBayesian Analysis of Piecewise Exponential ModelAnalysis of Competing-Risks Data
- References
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ESTIMATE <'label'> estimate-specification <(divisor=n)><, <'label'> estimate-specification <(divisor=n)>> <, …> </ options>;
The ESTIMATE statement provides a mechanism for obtaining custom hypothesis tests. Estimates are formed as linear estimable
functions of the form 
. You can perform hypothesis tests for the estimable functions, construct confidence limits, and obtain specific nonlinear
transformations.
Table 73.6 summarizes options available in the ESTIMATE statement. If the BAYES statement is specified, the ADJUST=, STEPDOWN, TESTVALUE, LOWER, UPPER, and JOINT options are ignored. The PLOTS= option is not available for the maximum likelihood analysis. It is available only for the Bayesian analysis.
Table 73.6: ESTIMATE Statement Options
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Option |
Description |
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Construction and Computation of Estimable Functions |
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Specifies a list of values to divide the coefficients |
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Suppresses the automatic fill-in of coefficients for higher-order effects |
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Tunes the estimability checking difference |
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Degrees of Freedom and p-values |
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Determines the method for multiple comparison adjustment of estimates |
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Determines the confidence level ( |
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Performs one-sided, lower-tailed inference |
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Adjusts multiplicity-corrected p-values further in a step-down fashion |
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Specifies values under the null hypothesis for tests |
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Performs one-sided, upper-tailed inference |
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Statistical Output |
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Constructs confidence limits |
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Displays the correlation matrix of estimates |
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Displays the covariance matrix of estimates |
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Prints the |
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Produces a joint F or chi-square test for the estimable functions |
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Requests ODS statistical graphics if the analysis is sampling-based |
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Specifies the seed for computations that depend on random numbers |
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For details about the syntax of the ESTIMATE statement, see the section ESTIMATE Statement in Chapter 19: Shared Concepts and Topics.